Discovering hidden causes using statistical evidence

Christopher Lucas

University of Edinburgh

Kenneth Holstein

University of Pittsburgh

Charles Kemp

Carnegie Mellon University

Abstract

People frequently reason about causal relationships and variables that cannot be directly observed. This paper presents results from an experiment in which participants used statistical information to make judgments about the number and base rates of hidden causes, as well as the forms of causal relationships in which those causes participated. Our data allow us to evaluate several models of hidden cause discovery, and reveal that people have different expectations about the forms of causal relationships than recent theories predict.